This invention relates to oil and gas reservoir management and, more specifically, to time lapse reservoir seismic signal processing.
Reservoir characterization and monitoring in the oil and gas field are important parts of reservoir management and hydrocarbon production. Effective reservoir management is a major goal of energy producing companies as they try to reduce finding costs, optimize drilling locations and increase financial returns. One technique that is attempted in this endeavor is time-lapse (also known as 4D) seismic monitoring. As fluids are extracted, swept, or injected through production and recovery, changes in the effective elastic properties of the reservoir rocks occur. The ability to monitor reservoir changes as a function of time by the use of seismic methods can lead to better location of production and infill wells, the possibility of locating unswept zones, and more efficient field maintenance, thus raising the overall value of the production lease.
In a two-dimensional approach, seismic monitoring has been examined in crosswell procedures. However, the repeated results have only been qualitatively compared, and two dimensional time-lapse surveys, so far, do not contain the type of information desired in modern reservoir management (for example, see Paulsson, et. al, 1994, The Leading Edge, incorporated herein by reference). Time-lapse 3D techniques have also been attempted, but they involve complex modeling procedures and require a great deal of processing without using direct data available in the survey itself. See, e.g., U.S. Pat. No. 4,969,130, incorporated herein by reference.
One problem in time lapse processing is that many conditions change over time, not just the changes in the state of the reservoir. For example, the locations of the source and receiver in the second survey will necessarily be different from those in the first. Further, the tide in a second marine survey may be higher or lower, as may the temperature of the air and water. Likewise, the specific characteristics of the sources and receivers used in the second survey will be different. Other differences, besides changes in reservoir state also occur, such as differences in the manner in which the two surveys are processed. Thus, there is a need for a method of dealing with the two surveys whereby processing differences do not detrimentally affect the result of the comparison.
For example, in gathering seismic data, a source is used to generate seismic waves which reflect from the reflectors in the earth (e.g. layer boundaries) and are received at receivers. In some cases, the source signature is a spike, although, in reality, it is not perfect. During its journey through the strata and reflectors, the signal shape is changed, and the reflection signal received at the receivers is, therefore, no longer a spike, or even close. Deconvolution is the process by which the shape of the reflection signal is xe2x80x9cwhitenedxe2x80x9d to recreate the spike shape of the data.
In another example, a broad, band-limited signal is used, which is zero phase. Deconvolution is used in such a case to remove the distortions caused by the earth.
In still another example, in performing the deconvolution in the frequency domain, all frequency samples are multiplied to bring them to an equal level, following the assumption that the source is a minimum phase signal, immediately rising to a peak and then dying. This is accomplished by autocorrelating the trace in the time domain multiple times, at a series of lag samples, which results in a generally symmetrical wavelet. The power spectrum of the wavelet is then analyzed, to determine what multipliers are needed at each frequency to flatten the frequency spectrum. This process is performed on a windowed basis, both along each trace, and across the record (as used herein, the term xe2x80x9crecordxe2x80x9d refers to, alternatively for example, a common receiver record, a CMP record, a common shot record, a stacked trace record, etc.) The autocorrelation is performed on various windows, and the results are averaged to give the spectrum. From that spectrum, the operators needed to flatten the spectrum are chosen. The operators are then applied to all of the traces used in the input. Typically, the window is about 10 times the length of the operator to be generated, measured in number of samples. The deconvolution process and the design of a deconvolution operator are well known in the art, and it is not limited to the frequency domain example, above. It is also routinely performed in the time domain. See, e.g., Yilmaz, Investigations in Geophysics, Vol. 2, Seismic Data Processing, Society of Exploration Geophysicists (1987) and references cited therein.
In performing deconvolution, it is important to design a deconvolution operator dependent upon the data of the survey, in order to account for the specific source signature, and other equipment distortions that occur in the data. Therefore, the data in each survey has been adjusted through the use of a specific optimum deconvolution operator which is not applicable to other surveys. The result of this difference in the use of the separate deconvolution operators in time-lapse surveys is that structure appears in the difference records when two records are subtracted. This result is undesirable. However, to date, no one has proposed a practical solution to the problem.
It is an object of the present invention to address the above problems.
It has been found that, contrary to earlier beliefs, a single deconvolution operator can be used on multiple sets of data, not only without detrimental results, but improving the quality of the processing of time-lapse comparisons of seismic surveys. Accordingly, in one aspect of the present invention, a method of deconvolution of multiple sets of seismic data from the same geographic area is provided, the method comprising: designing of a deconvolution operator dependent upon data from at least two of the sets of seismic data, wherein the at least two sets of seismic data were recorded at different times or calendar dates; applying the deconvolution operator in a deconvolution process to both of the at least two sets of data; and conducting further time-lapse processing to form a difference record.
According to one embodiment of the invention, the conducting of further time-lapse processing comprises: providing a first reflection event (for example, a wavelet) in the first seismic survey data set having a corresponding second reflection event in the second seismic survey data set, wherein the first reflection event and the second reflection event represent an unchanged portion of geologic structure in or near the reservoir and wherein the first reflection event is represented by a first set of event parameters and the second reflection event is represented by a second set of event parameters. Next, an acceptance threshold difference function between the first set of event parameters and the second set of event parameters is provided. Then, a crossequalization function is determined to apply to the second set of event parameters.
According to another aspect of the invention, the crossequalization function is determined such that, upon application of the crossequalization function to the second set of event parameters, a crossequalized set of event parameters is defined, and the difference between the first set of event parameters and the crossequalized set of event parameters is below the threshold difference function. Next, the crossequalization function is applied to a third reflection event, the third reflection event being related to the second data set, wherein a crossequalized third reflection event is defined, wherein the third reflection event has a corresponding fourth reflection event in the first data set, and wherein the third and fourth reflection events represent a changing portion of the reservoir.
Comparison of the crossequalized third reflection event to the fourth reflection event by subtracting the crossequalized third reflection event from the fourth reflection event results in the desired information.
According to a more specific example embodiment, said providing said acceptance threshold difference function comprises: iterative selection of event parameter modifications to the second set of event parameters, application of the event parameter modifications to the second set of event parameters, wherein a modified set of event parameters is defined, comparison of the modified set of event parameters to the first set of event parameters, wherein said iterative selection continues until a convergence is reached, and wherein the acceptance threshold difference function comprises the modified set of event parameters at convergence. Example event parameters comprise any combination of amplitude, phase, bandwidth, and time, or any of the foregoing individually.
According to another example embodiment, the determining of the crossequalization function comprises: iterative selection of event parameter modifications to the second set of event parameters, application of the event parameter modifications to the second set of event parameters, wherein a modified set of event parameters is defined, comparison of the modified set of event parameters to the first set of event parameters, and providing an acceptance threshold difference, wherein said iterative selection continues until a comparison result from said comparison designates a difference between the first set of event parameters and the modified set of event parameters below the acceptance threshold difference.
According to yet another example embodiment, said providing an acceptance threshold difference function comprises: providing a windowed trace difference between a time window of a first trace from the first seismic survey data set and a time window of a second trace from the second seismic survey data set, wherein the second trace includes reflection events corresponding to reflection events in the first trace and wherein the time window of the second trace is substantially the same as the time window of the first trace, and providing a ratio of the windowed trace difference over the time window of the first trace, and choosing the acceptance threshold difference to be less than the ratio.
The time windows in both the unchanging and changing portions of the reservoir have similar spectral characteristics. For example, if the data from the reservoir has a dominant frequency of 30 Hz, the time window used should be picked from an unchanging portion of the survey having a dominant frequency as close to 30 Hz as possible. Likewise, phase changes in the reservoir and the unchanging portion should be as close as possible. It is preferred, however, to err on the side of broader time windows. For example, if the reservoir dominant frequency is 30 Hz, a window having 35 Hz is considered preferable to one of 25 Hz. Such bandwidth error of less than about 25% in frequency bandwidth is believed to yield adequate results. Best results should be seen when bandwidth error is below 10%.
In still another embodiment, said providing an acceptance threshold difference function comprises: providing a windowed trace difference between a time window of the square of a first trace from the first seismic survey data set and a time window of the square of a second trace from the second seismic survey data set, wherein the second trace includes reflection events corresponding to reflection events in the first trace and wherein the time window of the second trace is substantially the same as the time window of the first trace, and providing a ratio of the windowed trace difference over the time window of the square of the first trace, choosing the acceptance threshold difference to be less than the ratio.
In still another embodiment, said applying the crossequalization function to a third reflection event in the second data set comprises convolution between the crossequalization function and the third reflection event in the second data set, said first data set comprises a trace from a seismic receiver. Alternatively, said first data set and said second data set comprise a summed set of traces from a set of seismic receivers, or CMP (xe2x80x9ccommon mid-pointxe2x80x9d) data. In still a further embodiment, said first data set and said second data set comprise shot data. In further alternatives, said first data set and said second data set comprise prestack data or migrated data.
In many embodiments, said first data set and said second data set are subjected to equivalent prestack processes. For example, in addition to the deconvolution described above, in some embodiments the first data set uses the same designature process as the second data set, the same noise attenuation processing steps as second data set, and the same multiple attenuation processing as the second data set. Further, in many embodiments, the same DMO operator is used on first data set as on the second data set, and migration on the first data set is conducted with the same velocity field as migration on the second data set.
Finally according to a further aspect of the invention, a method is provided for performing time-lapse seismic survey signal processing, the method comprising: performing a set of processing steps on the first survey; performing the set of processing steps on the second survey, wherein the set of processing steps is dependent upon a set of seismic signal parameters; choosing at least one of the set of parameters by a selection process dependent upon data from both surveys; and (b) applying the at least one of the sets of parameters in the at least one of the set of processing steps to both the first survey and the second survey.